ecg_pacemaker_detector.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsKJsKanN7Ev"
      },
      "source": [
        "## 0. Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfz1WkyhOGxU"
      },
      "source": [
        "A pacemaker is a small device that's placed (implanted) in the chest to help control the heartbeat. It's used to prevent the heart from beating too slowly. Implanting a pacemaker in the chest requires a surgical procedure. The presense of a pacermaker on ECG is manifested by stimulation artifacts as demonstrated in the image bellow:\n",
        "![Screen Shot 2022-05-08 at 22.04.55.png](data:image/png;base64,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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmyXPas0OMGD"
      },
      "source": [
        "In this mini-project you will be asked to create an algorithm to classify ECG records with a pacemaker and analyse its perofrmance. \n",
        "\n",
        "You can write your code directly in this notebook.\n",
        "\n",
        "Please pay attention to writing a clean code following best coding practices."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "klVYkC1suYz5"
      },
      "source": [
        "## 1. Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEFSCFAPucjd",
        "outputId": "b785f4ec-db20-49c3-d9b6-5e7e45bdf954"
      },
      "outputs": [],
      "source": [
        "# download the PTB-XL dataset\n",
        "!wget https://physionet.org/static/published-projects/ptb-xl/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.1.zip\n",
        "## unzip folders\n",
        "!unzip ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.1.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GG9jfrwauq9h",
        "outputId": "c19b54b3-95a8-420c-a1a6-090a741437e2"
      },
      "outputs": [],
      "source": [
        "# install a package to read and process signals in WFDB format\n",
        "!pip install wfdb\n",
        "# install a package to plot\n",
        "!pip install matplotlib==3.1.3\n",
        "# install neurokit2  package to analysis and processing\\cleaning ecg\n",
        "!pip install neurokit2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5G2z-y_iwaY6"
      },
      "source": [
        "## 2. Load libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hq8vC3LLualq"
      },
      "outputs": [],
      "source": [
        "import ast\n",
        "import wfdb\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from zlib import crc32\n",
        "from pathlib import Path\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import *\n",
        "import scipy.fftpack\n",
        "import neurokit2 as nk\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.backend import clear_session"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QovEt4q1E8S1"
      },
      "source": [
        "## 3. Auxiliary Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q4dYt3wbE9tn"
      },
      "outputs": [],
      "source": [
        "# plot ECG signal from a signal lead\n",
        "def plot_lead(lead_signal, sampling_rate):\n",
        "    min_y = 0.1 * (lead_signal.min() // 0.1)\n",
        "    max_y = 0.1 * ((lead_signal.max() // 0.1) + 1)\n",
        "    min_x = 0\n",
        "    max_x = len(lead_signal) / sampling_rate\n",
        "    xbins = np.arange(len(lead_signal)) / sampling_rate\n",
        "\n",
        "    fig = plt.figure(figsize=(25, 1.5))\n",
        "    ax = fig.add_subplot(111)\n",
        "    ax.plot(xbins, lead_signal)\n",
        "    ax.set_xlim(0, max_x)\n",
        "\n",
        "    xmajor = np.arange(min_x, max_x + 0.04, 0.2)\n",
        "    xminor = np.arange(min_x, max_x + 0.04, 0.04)\n",
        "    ymajor = np.arange(min_y, max_y + 0.1, 0.5)\n",
        "    yminor = np.arange(min_y, max_y + 0.1, 0.1)\n",
        "\n",
        "    ax.set_xticks(xminor, minor=True)\n",
        "    ax.set_xticks(xmajor, minor=False)\n",
        "\n",
        "    ax.set_yticks(yminor, minor=True)\n",
        "    ax.set_yticks(ymajor, minor=False)\n",
        "\n",
        "    ax.grid(which=\"minor\", color=\"red\", linewidth=0.3)\n",
        "    ax.grid(which=\"major\", color=\"red\", linewidth=0.5)\n",
        "    ax.set_xticklabels([])\n",
        "    ax.set_yticklabels([])\n",
        "\n",
        "    return fig, ax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fQS8S5w1FAxf"
      },
      "outputs": [],
      "source": [
        "# create a table (dataframe) based on the downloaded PTB-XL dataset\n",
        "def gen_df_base(ptbxl_root: Path) -> pd.DataFrame:\n",
        "    ptbxl_root = Path(ptbxl_root)\n",
        "    df_ptbxl = pd.read_csv(ptbxl_root / \"ptbxl_database.csv\", dtype={\"patient_id\": int})\n",
        "    df_scp = pd.read_csv(ptbxl_root / \"scp_statements.csv\", index_col=0)\n",
        "\n",
        "    def aggregate_subclass(y_dic, agg_df):\n",
        "        tmp = []\n",
        "        for key in y_dic.keys():\n",
        "            if key in agg_df.index:\n",
        "                tmp.append(agg_df.loc[key].diagnostic_subclass)\n",
        "        return list(set(tmp))\n",
        "\n",
        "    def aggregate_class(y_dic, agg_df):\n",
        "        tmp = []\n",
        "        for key in y_dic.keys():\n",
        "            if key in agg_df.index:\n",
        "                tmp.append(agg_df.loc[key].diagnostic_class)\n",
        "        return list(set(tmp))\n",
        "\n",
        "    def aggregate_diagnostic(y_dic):\n",
        "        tmp = []\n",
        "        for key in y_dic.keys():\n",
        "            tmp.append(key)\n",
        "        return list(set(tmp))\n",
        "\n",
        "    df_ptbxl.scp_codes = df_ptbxl.scp_codes.apply(lambda x: ast.literal_eval(x))\n",
        "    df_scp = df_scp[df_scp.diagnostic == 1]\n",
        "    df_ptbxl[\"diagnostic_subclass\"] = df_ptbxl.scp_codes.apply(lambda x: aggregate_subclass(x, df_scp))\n",
        "    df_ptbxl[\"diagnostic\"] = df_ptbxl.scp_codes.apply(lambda x: aggregate_diagnostic(x))\n",
        "    df_ptbxl[\"diagnostic_class\"] = df_ptbxl.scp_codes.apply(lambda x: aggregate_class(x, df_scp))\n",
        "\n",
        "    return df_ptbxl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUJV-IZ0FEPb"
      },
      "source": [
        "Prepare data table (dataframe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XEchSjqzFFVB"
      },
      "outputs": [],
      "source": [
        "ptbxl_root = Path(\"/content/ptb-xl-a-large-publicly-available-electrocardiography-dataset-1.0.1\")\n",
        "df = gen_df_base(ptbxl_root)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-QJdIZ-FRqe"
      },
      "source": [
        "In the data table (df) the diagnostic labels are found in the column \"diagnostic\". The pacemaker is denoted by the string \"PACE\"; if the string \"PACE\" is among the diagnostic labeles in a specific row, it means that the corresponding ECG record belongs to a patient with a pacemaker."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2iQ2WNrFVsL"
      },
      "source": [
        "## Task 1:\n",
        "\n",
        "* Add a new column \"has_pacemaker\" to the datadrame df with True for ECG records with a pacemaker and False otherwise.\n",
        "* Can you think why using the \"diagnostic\" column to infere the pacemaker label may be better than using the \"pacemaker\" column directly? Explain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4IqmONqnGVJC"
      },
      "outputs": [],
      "source": [
        "# check which rows the contain the 'PACE' string in diagnostic column and assign to a new coulmn 'has_pacemaker' True if exsit otherwise (o.w) False\n",
        "df.insert(31,\"has_pacemaker\",df['diagnostic'].str.contains('PACE', regex=False))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xx03LIPNidT9"
      },
      "source": [
        "The reasons why we would like to infer the pacemaker label from the “diagnostic” rather than the “pacemaker” column are:\n",
        "\n",
        "* First, We wish to compare the quality of the two columns ('scp_codes'/'diagnostic' and 'pacemaker'), in the dataset documentation, we can see that the 'diagnostic' column infers from the 'report' column, which contains more about the patient information rather than the 'pacemaker' column.\n",
        "* Second, the SCP-ECG statements are written as a dictionary with entries of the form 'statement: likelihood', where the likelihood is set to 0 if unknown, therefore we have a softer decision regarding the presence of a pacemaker, rather than just 'NaN' or 'ja' (yes in German). The motivation for using such a likelihood mechanism is that for some physicians the pacemaker signal could be described as other diagnostic due to 'pacemaker' failure, problem in the device ECG, or a wrong ECG test and vice versa.\n",
        "    * Remark: now, there are just two values of pacemaker likelihood, 0 and 100. Nevertheless, this structure lets us have the ability in the future to have more decision degrees of freedom (soft) in our analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEp0REZiyrIy"
      },
      "source": [
        "## Task 2: Perform exploraroty analysis of the data in the table\n",
        "\n",
        "* Analyse the distribution of age, sex, weight, recording date.\n",
        "* Can you think of other interesting questions that could be asked about the data in the table?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lO2kIdvVKuhM",
        "outputId": "4de259ae-0277-4f8f-b34d-87741c8a37e8"
      },
      "outputs": [],
      "source": [
        "# taking just the relevant data \n",
        "df_display=df[['age','weight','recording_date']]\n",
        "# convert the recording_date to displayable format\n",
        "df_display[\"recording_date\"] = df_display[\"recording_date\"].astype(\"datetime64\")\n",
        "df_display[\"recording_date\"].groupby([df_display[\"recording_date\"].dt.hour])\n",
        "df_display.hist(bins=15,figsize=(15,15))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5u5pI1CRItME"
      },
      "source": [
        "* **Age**: \n",
        "  * Mean: 60\n",
        "  * STD: 17\n",
        "  * The values span from: 2 to 85 years old, while statiscally speaking the ages are mainly between 43-77 years old.\n",
        "* **Sex**: \n",
        "  * 48.0% Female \n",
        "  * 52.0% Male\n",
        "* **Weight**: \n",
        "  * Mean: 71 \n",
        "  * STD: 16\n",
        "  * The weight span from: 5 to 250 kg, while statiscally speaking the weight are mainly between 55-87 kg.\n",
        "* **Recording Date**:\n",
        " * We can see that the months and the days are likely to be a bit random, most of the recording done between 1990-1997 and between 08:00-14:00."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EupMA6Skcj13"
      },
      "source": [
        "* There are more insightful statistics that we can derive from the data:\n",
        "  *  What is the percentage of true pacemaker labels among the all dataset?\n",
        "    * 1.34%\n",
        "    * We see that our dataset in relatively lean for pacemaker classification, (this insight will appear also in Task 4 as part of our performance analysis).\n",
        "  * How many of the true pacemaker labels are inferred with other diagnostics among the total true pacemaker label? and among the all dataset?\n",
        "    * 6.8% from the true pacemaker\n",
        "    * 0.09% from the all dataset\n",
        "    * This conclusion can tell if we can say something about the \n",
        "      pacemaker miss-detection from other diagnostics—in this case it will be very hard.\n",
        "  * How many dataset samples are reassured by a physician's second opinion?\n",
        "    * 2.5%\n",
        "  * How many samples within the true pacemaker labels have been reassured by a physician's second opinion?\n",
        "    * 0%\n",
        "  * **Diversity**: May effect on how our data is bias and also about the pacemaker classification results—we will examine the percentage among the all dataset and the percentage from the pacemaker true labels:\n",
        "    * **Patients**: we wish to make sure that the data (has_pacemakr: false\\true) data is diverse for example that all the data has been taken from the same person (it the worst case).\n",
        "      * How many patients the dataset contains?\n",
        "        * 18885 different patients (out of 21837)\n",
        "        * typical (mean) appearance: 1.15\n",
        "        * appearance deviation: 0.5\n",
        "      * How many patients among the true label pacemaker dataset contains?\n",
        "        * 237 different patients (out of 296)\n",
        "        * typical (mean) appearance: 1.24\n",
        "        * appearance deviation: 0.6\n",
        "      * Conclusion: we see that our patient data is diverse.\n",
        "    * **ECG device**:\n",
        "        * How many ECG devices the dataset contains?\n",
        "          * 11 different ECG device (out of 21837 exams)\n",
        "          * The maximum ECG device which appears 28.11% from the total tests is: 'CS100-3' then devices 'CS-12' and 'AT-6-C-5.5' with 18% and the 8 rest devices are 36%.\n",
        "        * How many patients among the true label pacemaker dataset contains?\n",
        "          * 10 different sites (out of 296 exams and 11 total sites)\n",
        "          * The maximum ECG device which appears 65.5% from the total tests is: 'CS100-3' then device 'CS-12' with 8% and the 8 rest devices are 27%.\n",
        "        * Conclusion: we see that our data *(mainly the 'has_pacemaker')* is highly dependent in the performance of device 'CS100-3'.\n",
        "    * **Site**:\n",
        "        * How many sites the dataset contains?\n",
        "          * 51 different sites (out of 21837 exams)\n",
        "          * The maximum site which appears 41% from the test is: 0 then site 1 with 28% and 2 with 23% the 48 rest site are just 8%.\n",
        "        * How many patients among the true label pacemaker dataset contains?\n",
        "          * 9 different sites (out of 296 exams and 51 total sites)\n",
        "          * The maximum site which appears 66% from the test is: 0 then site 1 with 13% and 2 with 10% the 6 rest sites are just 11%.\n",
        "        * Conclusion: we see that our data (mainly the 'has_pacemaker') is highly dependent on site 0 and partly in site 1 and 2.\n",
        "    * **Nurse**:\n",
        "        * How many nurses the dataset contains?\n",
        "          * 12 different nurses (out of 21837 exams)\n",
        "          * The maximum nurse which appears 38% from all the tests is: 0 then site 1 with 26% and the rest nurses sharing the other 35% evenly.\n",
        "        * How many patients among the true label pacemaker dataset contains?\n",
        "          * 11 different sites (out of 296 exams and 12 total sites)\n",
        "          * The maximum nurse, which appears 66.8% from all the tests is: 0 then site 1 with 12.5% and the rest nurses sharing the other 20.7% evenly.\n",
        "        * Conclusion: we see that our data (mainly the 'has_pacemaker') is highly dependent on nurse 0 and partly in nurse 1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-FPP_Pw0qw-"
      },
      "source": [
        "***Remark***: I don't put the analysis source code for the above section—I mainly used in value_counts(), mean(), std() dataframe functions and a simple percentage calculations. My data exploration orientation was to analysis the mean and std in each section and if I see large std relative to the mean I apply values_count() and some statistics to gain better understating about the different cases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmP7Y6SdS2KY"
      },
      "source": [
        "### Visualize ECG records w\\o pacemaker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eh_dyNxkS65_"
      },
      "source": [
        "Run the following cells to examine examples with and without a pacemaker. Do you see the difference?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gplZDvB_8uEg"
      },
      "source": [
        "**Indeed, we can see a visible delta function which may imitate the Sinoatrial Node action (QRS segment).**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "-Bc1uVaGS8o8",
        "outputId": "65f4f91f-f95b-4628-8c4b-315e5bdab8eb"
      },
      "outputs": [],
      "source": [
        "filename = df[df[\"has_pacemaker\"]][\"filename_hr\"].values[0]\n",
        "record = wfdb.rdrecord((ptbxl_root/filename).as_posix())\n",
        "fix,ax  = plot_lead(record.p_signal[:,2],record.fs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "YNFt0m0NS9LR",
        "outputId": "01ee38dd-f02f-4176-d790-ef1fa9ed2f59"
      },
      "outputs": [],
      "source": [
        "filename = df[~df[\"has_pacemaker\"]][\"filename_hr\"].values[6]\n",
        "record = wfdb.rdrecord((ptbxl_root/filename).as_posix())\n",
        "fix,ax  = plot_lead(record.p_signal[:,2],record.fs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkfRrKj8w7ZI"
      },
      "source": [
        "## Split Data - Train and Test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzxtI9Kw-NNp"
      },
      "source": [
        "**Remark**: According to the dataset/article: (https://www.nature.com/articles/s41597-020-0495-6#Sec5)\n",
        "* The ninth (2194 samples, 26 samples with true pacemaker labels) and tenth (2204 samples, 28 samples with true pacemaker labels) fold are folds with a particularly high label quality that are supposed to be used as validation and test sets.\n",
        "* The relation between the true pacemaker labels remain relative the same as the total folds number.\n",
        "* \"We propose to use the tenth fold, which is ensured to contain only ECGs that have certainly be validated by at least one human cardiologist and are therefore presumably of highest label quality, to separate a test set that is only used for the final performance evaluation of a proposed algorithm. The remaining nine folds can be used as training and validation set and split at one’s own discretion, potentially utilizing the recommended fold assignments. As the ninth and the tenth fold satisfy the same quality criteria, we recommend to use the ninth fold as validation set.\" - more explanation regard the folds selection and preferences inside the article."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E7gxUOcP9cDr"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from pathlib import Path\n",
        "import wfdb  \n",
        "import numpy as np\n",
        "from typing import Tuple\n",
        "\n",
        "# using the 100 Hz records\n",
        "sampling_rate: int = 100\n",
        "# used the Cross-Validation Folds recommandation from the dataset website\n",
        "test_fold: int = 10\n",
        "\n",
        "\n",
        "def load_raw_data(df: pd.DataFrame, sampling_rate: int, path: Path) -> np.ndarray:\n",
        "    if sampling_rate == 100:\n",
        "        data = np.array([wfdb.rdsamp(str(path) + \"/\" + f) for f in df.filename_lr])\n",
        "    else:\n",
        "        data = np.array([wfdb.rdsamp(str(path) + \"/\" + f) for f in df.filename_hr])\n",
        "    data = np.array([signal for signal, meta in data])\n",
        "    return data\n",
        "\n",
        "\n",
        "def data_prep(\n",
        "    df: pd.DataFrame, path: Path\n",
        ") -> Tuple[np.ndarray, pd.DataFrame, np.ndarray, pd.DataFrame]:\n",
        "    raw_ecg_signal = load_raw_data(df, sampling_rate, path)\n",
        "    # Train\n",
        "    raw_ecg_signal_train = raw_ecg_signal[np.where(df.strat_fold != test_fold)]\n",
        "    labels_train = df[(df.strat_fold != test_fold)].has_pacemaker\n",
        "    # Test\n",
        "    raw_ecg_signal_test = raw_ecg_signal[np.where(df.strat_fold == test_fold)]\n",
        "    labels_test = df[df.strat_fold == test_fold].has_pacemaker\n",
        "    return raw_ecg_signal_train, labels_train, raw_ecg_signal_test, labels_test\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aT_MXIV3yRY-",
        "outputId": "19045e06-a1e1-418a-d023-d05492eff043"
      },
      "outputs": [],
      "source": [
        "# call the data prep function\n",
        "data_train, label_train, data_test, label_test = data_prep(df, ptbxl_root)\n",
        "\n",
        "## CONVERTING INPUT IMAGES TO CONV LAYER INPUT FORMAT\n",
        "xtrain = data_train.reshape(data_train.shape[0], data_train.shape[1], data_train.shape[2], 1)\n",
        "xtest = data_test.reshape(data_test.shape[0], data_test.shape[1], data_test.shape[2], 1)\n",
        "\n",
        "## converting output into interger values False : 0, True : 1\n",
        "ytrain = label_train.map({False : 0, True : 1})\n",
        "ytest = label_test.map({False : 0, True : 1})\n",
        "\n",
        "## one hot encoding of output to make model ready data\n",
        "y_train = tf.keras.utils.to_categorical(ytrain , num_classes=2)\n",
        "y_test = tf.keras.utils.to_categorical(ytest , num_classes=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sroq7N3XxIKf"
      },
      "source": [
        "## Preprocessing ECG data - Clean/Peak Enhancement signal using extarnal libary (Optional):\n",
        "https://neurokit2.readthedocs.io/en/latest/index.html\n",
        "* Using butterworth IIR filter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "ZAO4_IneaK_N",
        "outputId": "812ef109-ee10-4213-eed4-39039bf82561"
      },
      "outputs": [],
      "source": [
        "# Define a new function\n",
        "def ecg_pacemaker_preprocessing(ecg_signal,sampling_rate=100):\n",
        "    ecg_cleaned = nk.ecg_clean(ecg_signal, sampling_rate)\n",
        "    return ecg_cleaned\n",
        "\n",
        "wfdb_sample_data=xtrain[100,:,0]\n",
        "sampling_rate=100\n",
        "wfdb_sample_data_clean= ecg_pacemaker_preprocessing(wfdb_sample_date,sampling_rate)\n",
        "\n",
        "## plot loss and accuracy\n",
        "fig, ax = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "ax[0].plot(wfdb_sample_data, label = 'raw data')\n",
        "ax[0].set_title(\"ECG data-PTB-XL\")\n",
        "ax[0].legend()\n",
        "\n",
        "ax[1].plot(wfdb_sample_data_clean, label = 'clean data')\n",
        "ax[1].set_title(\"ECG data clean-NeuroKit\")\n",
        "ax[1].legend()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h6OoJ7KnDqsd"
      },
      "source": [
        "**Remarks**: \n",
        "* In case we wish to apply nk.ecg_clean on the entire data use:\n",
        "  * xtrain_clean=np.apply_along_axis(ecg_pacemaker_preprocessing, 1, xtrain)\n",
        "* I checked the performance of the model with the clean data—I don't see any improvement—this has been one of my ways to examine methods to get better confusion matrix."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksPB8rMvw-ZG"
      },
      "source": [
        "## Task 3 \n",
        "\n",
        "Build a classifier to descriminate between ECG records with and without a pacemaker\n",
        "* You can use any approach you want.\n",
        "* Solving the problem with a Convolution Neural Network (CNN) will grant you with extra credit (Hint: you can adapt standard architectures, however a simple network with few convolitional and maxpool layers will give good enough results for this problem).\n",
        "* If you choose the CNN approach you can use either Keras or Pytorch (If you donj't have preferences for a specific framework, Keras is preferable) .\n",
        "* Explain in details your solution and be ready to defend it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nO-VciZWKWgh"
      },
      "outputs": [],
      "source": [
        "def conv_model(input_shape):\n",
        "\n",
        "    model = Sequential() ## creating a sequential model\n",
        "\n",
        "    ## 1st convolution layer with 16-> filter and (3,3) -> kernel size\n",
        "    model.add(Conv2D(16, (3,3), activation = 'relu', input_shape =input_shape))\n",
        "    ## adding maxpool layer with size/pool size (2,2)\n",
        "    model.add(MaxPooling2D(pool_size =(2,2)))\n",
        "\n",
        "    ## 2nd convolution layer with 16-> filter and (3,3) -> kernel size\n",
        "    model.add(Conv2D(16, (3,3), activation = 'relu'))\n",
        "    ## adding maxpool layer with size/pool size (2,2)\n",
        "    model.add(MaxPooling2D(pool_size = (2,2)))\n",
        "    \n",
        "    ## flattening the layer\n",
        "    model.add(Flatten())\n",
        "\n",
        "    ## dense layer to finetune model\n",
        "    model.add(Dense(128, activation='relu'))\n",
        "    ## output layer\n",
        "    model.add(Dense(2, activation='sigmoid'))\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTzIQtPVz0Ta"
      },
      "source": [
        "* **Model explanation**:\n",
        "\n",
        "  * I've created a convolution neural network based model with 2 convolution layers and 2 maxpooling layer.\n",
        "  * Applied flat layer to get all features into one dimension. \n",
        "  * Applied one dense layer.\n",
        "  * To fine tune the model and finally output layer with 2 neurons (True, False) is being added.\n",
        "  * Throughout the model, I have used relu activation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dhX_U_BRzbVI",
        "outputId": "30df48a3-6d7a-4f93-c143-572272b24f25"
      },
      "outputs": [],
      "source": [
        "## get model object\n",
        "model = conv_model((xtrain.shape[1], xtrain.shape[2],1))\n",
        "\n",
        "## compiling model with log loss/binary cross entropy, accuracy score , and adam optimizer\n",
        "model.compile(loss = \"binary_crossentropy\", metrics = ['accuracy'], optimizer='adam')\n",
        "\n",
        "## training model for 10 epochs\n",
        "history = model.fit(xtrain, y_train, validation_data = (xtest, y_test), batch_size=32, epochs = 10, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKsdDjMJ2qwx",
        "outputId": "05df36a6-8cb0-4230-a2d4-9b1a598bd9dc"
      },
      "outputs": [],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "fihTyhC0zwVB",
        "outputId": "8b7eaa22-469e-409d-fc62-421a2765082e"
      },
      "outputs": [],
      "source": [
        "## plot loss and accuracy\n",
        "fig, ax = plt.subplots(1, 2, figsize=(15,5))\n",
        "\n",
        "ax[0].plot(range(1,11), history.history['loss'], label = 'train loss')\n",
        "ax[0].plot(range(1,11), history.history['val_loss'], label = 'test loss')\n",
        "ax[0].set_title(\"loss plot\")\n",
        "ax[0].legend()\n",
        "\n",
        "ax[1].plot(range(1,11), history.history['accuracy'], label = 'train accuracy')\n",
        "ax[1].plot(range(1,11), history.history['val_accuracy'], label = 'test accuracy')\n",
        "ax[1].set_title(\"accuracy plot\")\n",
        "ax[1].legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcFeDv9B1w2w"
      },
      "source": [
        "## Task 4\n",
        "Analyse the performance of your classifier\n",
        "* Plot the ROC curve.\n",
        "* Provide the standard performance metrics: specificity, sensitivity, AUC.\n",
        "* Suggest a working point threshold.\n",
        "* Analyse the errors of your model: what are the reasons for false pos/neg?\n",
        "* Discuss how would you improve your classifier if you had more time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6rm3wPwW4XFL"
      },
      "outputs": [],
      "source": [
        "yprediction_train = model.predict(xtrain) ## getting train data prediction as probabilites\n",
        "ytrain_pred = [np.argmax(i) for i in yprediction_train] ## getting a list of output values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DatLvOyq1DYo"
      },
      "outputs": [],
      "source": [
        "yprediction = model.predict(xtest) ## getting test data prediction as probabilites\n",
        "ytest_pred = [np.argmax(i) for i in yprediction] ## getting a list of output values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kgJpUnF_5Zry",
        "outputId": "8b838475-ae97-47c7-eed7-3475763450e8"
      },
      "outputs": [],
      "source": [
        "print(classification_report(ytest, ytest_pred))\n",
        "print(classification_report(ytrain, ytrain_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_1FzBlEb3vaW",
        "outputId": "65605b4b-8d75-426d-ee0a-75c02a1f1ab1"
      },
      "outputs": [],
      "source": [
        "## getting tpr, fpr scores wit threshold for test data\n",
        "fpr_te, tpr_te, threshold_te = roc_curve(ytest,  yprediction[:, 1])\n",
        "\n",
        "auc_test = auc(fpr_te, tpr_te)\n",
        "print(f\"auc score for test data is {auc_test:.4f}\")\n",
        "\n",
        "optimal_idx = np.argmax(tpr_te - fpr_te)\n",
        "optimal_threshod_test = threshold_te[optimal_idx]\n",
        "print(\"Test Threshold value is:\", optimal_threshod_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4ssrTA14c6Q",
        "outputId": "73cb0d8e-7443-444a-8f25-b3ee5104aa21"
      },
      "outputs": [],
      "source": [
        "## getting tpr, fpr scores wit threshold for train data\n",
        "fpr_tr, tpr_tr, threshold_tr = roc_curve(ytrain,  yprediction_train[:, 1])\n",
        "\n",
        "auc_train = auc(fpr_tr, tpr_tr)\n",
        "print(f\"auc score for train data is {auc_train:.4f}\")\n",
        "\n",
        "optimal_idx = np.argmax(tpr_tr - fpr_tr)\n",
        "optimal_threshod_train = threshold_tr[optimal_idx]\n",
        "print(\"Train Threshold value is:\", optimal_threshod_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "gOk_37K84J2k",
        "outputId": "14906c2c-8fef-47bb-cf80-21227c005e50"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.plot(fpr_tr, tpr_tr, label = \"train\")\n",
        "plt.plot(fpr_te, tpr_te, label = \"test\")\n",
        "plt.legend()\n",
        "plt.ylabel('True Positive Rate/Sensitivity')\n",
        "plt.xlabel('False Positive Rate/(1-Specificity))')\n",
        "plt.title('ROC Curve')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_hLN6Yf-6U4I",
        "outputId": "4550fa0f-2481-4239-c125-9e75ea6489fb"
      },
      "outputs": [],
      "source": [
        "print(\"Train Confusion Matrix\")\n",
        "print(confusion_matrix(ytrain, ytrain_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VOMMr0A7Shp",
        "outputId": "9a884c83-f71f-47fa-c69d-d381eccda2bc"
      },
      "outputs": [],
      "source": [
        "print(\"Test Confusion Matrix\")\n",
        "print(confusion_matrix(ytest, ytest_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_L5gk9Oz8sfo"
      },
      "source": [
        "[*Note—Majority Class (No peacemaker), Minority Class (Yes peacemaker)*]\n",
        "\n",
        "* **Analysis Model Errors**:\n",
        "  * The model has trained the training data in such as way that the minority class (Peacemaker—Yes) has 100% accuracy whereas the majority class(Peacemaker - No) has also generated 99.99% accuracy. \n",
        "  * Surprisingly there is no misclassification for minority class and False Positive is 12, whereas False negative is 0.\n",
        "  * When we compare the train result with the test data, there is a huge misclassification for minority class which indicates model is overfitting \n",
        "  (not able to replicate the results from train data into test data for minority class) resulting the False Negative as 12 (41%), whereas the majority class has no effect(False Positive 0.22%) and performing well.\n",
        "  * Overall, the model performance on test data as compared to training data is not satisfactory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qybjAtG0-oNo"
      },
      "source": [
        "* **Model Improvement**:\n",
        "\n",
        "  * We can improve the model by adding more minority class data to the dataset, so that we can make the classes a balanced dataset to some extent.\n",
        "\n",
        "  * We can add or remove layers from the model and experiment different set of parameters to get an optimal model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SckO4hs_fCd"
      },
      "source": [
        "## Task 5\n",
        "\n",
        "Model deployment\n",
        "\n",
        "\n",
        "The model you developed and tested is going to be deployed in a hospital. Every ECG record obtained in this hospital should pass through the model and output a notification in case a pacemaker is present. Note that the real production envrionment is noisy and you will need to deal with different outliers and exceptions.\n",
        "\n",
        "* Discuss how would you deploy the model in production; suggest a high level design of the production pipiline that cover possible edge cases.\n",
        "* How would you monitor the performance of your model? Which caveats would you expect?\n",
        "* **Bonus**: implement a production level system that wraps your model. Specifially, given an input signal and its metadata (e.g. age, sex, scan date, etc) the system checks if the patient is above 18 years old, if yes it applies the pacemaker algorithm and returns an output object containing the algorithm result (True/False), patient age, and scan date; otherwise it throws an exception and returns the output object with patient age, scan date and empty algorithm result."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6Gx8qNnyywW"
      },
      "source": [
        "* *Discuss how would you deploy the model in production; suggest a high level design of the production pipeline that cover possible edge cases*:\n",
        "\n",
        "  In case we wish to deploy model in production we need to consider the \n",
        "  following:\n",
        "  * Tracking:\n",
        "    * Using version control—to tracking the: data, model architectures, hyperparameters, and external software. Specifically, we can use model registry model location, information on how to reproduce the training environment, references to the training data and more.\n",
        "    * DevOps:\n",
        "      * CI/CD—automate builds and deployments.\n",
        "      * Package models appropriately—Model Deployed as a Service—create thin microservice thin that consumes features as input and serves model predictions.\n",
        "  * Monitoring:\n",
        "      * Develop and deploy a logging module—also send some alert in case there are some errors.\n",
        "      * Measure the system reliability—uptime and how the system response to requests.\n",
        "  * Reliability:\n",
        "    * Deploy on reliable infrastructure: container—easy to develop a CI/CD, region flexibility and more.\n",
        "    * Unit tests and end-to-end tests.\n",
        "\n",
        "  After we understood the building blocks above, we wish to discuss on how and with which tools we prefer to use.\n",
        "\n",
        "  The ECG application must be reliable therefor deploy it on-premises isn't a good idea, moreover in case we wish to do so, the IT team in the hospital must be professional, and we assume that it's may not be the case. Therefore, using not-manage services, as self develop Kubernetes platform can be irrelevant.\n",
        "  Hence, we choose to work with one of the largest cloud providers: AWS, Azure, GCP self-managed services to reassure reliability, cost and performance.\n",
        "\n",
        "  *Assumptions*:\n",
        "  1. The data/metadata send to cloud from the device.\n",
        "  2. We need to consider multiple devices (5000+).\n",
        "  3. We have development/staging/production environment.\n",
        "\n",
        "  *High Level Design*:\n",
        "  1. Development environment :\n",
        "    * Working we source control and CI—test/build the relevant docker images.\n",
        "    * Build the relevant pipeline: \n",
        "      * Data—validation, preprocessing, model training, model analysis.\n",
        "    * For this case we need to use some 'cold-storage' (Azure Blob, S3), to save the model that we serve to the production environment, the data in each phase—we need to keep the data dirty for further use.\n",
        "    * For each one of these pipelines, we need to write automation and testing.\n",
        "  2. Staging/production environment:\n",
        "    * contain the same data pipeline as above with adding module of data extraction—fetch and organized the data in the relevant directory, db or storage.\n",
        "    * Model registry, save a version of the trained model and the metadata.\n",
        "    * Deploy the predication service with relevant updated model.\n",
        "    * Performance monitoring module—evaluate the reality and accuracy of the predication service and model.\n",
        "  3. We wish to add to our data pipeline, a DSP module which use filtering to clean the ECG signal.\n",
        "  4. To send the data in real time we prefer to use Websocktes, \n",
        "  updates are sent immediately when they are available, and we don't need to wait—it can also serve as a base protocol on which to implement device management frameworks, to configure the device from remote.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thGLxkGEByiI"
      },
      "source": [
        "*How would you monitor the performance of your model? Which caveats would you expect?*\n",
        "* Performance monitoring:\n",
        "  *  Customize triggers to notify you when there are significant changes in metrics such as accuracy, precision.\n",
        "  * Try to understand if the performance aren't stable and have a specific pattern, if it's the case we need to design more robust model that will have better overall performance.\n",
        "  * Check and monitoring on data that the model performance are poor.\n",
        "  * Pay attention to the metadata values: change in the ECG devices, recording date—nurse and hospital team aren't focus enough and more metadata that indicate that we need to tune our models.\n",
        "* Which caveats would you expect?:\n",
        "  * ECG data access and model sharing.\n",
        "  * Limited flexibility of pre-existing IT infrastructures and teams.\n",
        "  * The datasets cloud be fetched from a single source, therefore an overfitting and generalizing poorly can be occurred—as we saw in our exploration.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeVikVn4Jjw0"
      },
      "source": [
        "### **Bonus**:\n",
        "* **Framework and Infrastructure**:\n",
        "  * Flask framework for the web application.\n",
        "  * Poetry, Python for dependency management and packaging.\n",
        "  * AWS EC2 t2.micro instance.\n",
        "\n",
        "* **Major Steps**:\n",
        "  1. Develop the model on jupyter notebook (colab) .\n",
        "  2. Setup a develop enviorment using Keras container enviorment 'honghu/keras' via WSL 2 docker enviorment (optiontional).\n",
        "  3. Install the releveant packages and develop the web application server-side code.\n",
        "    * Using flake8 to check the code style and quality (pep8).\n",
        "    * Still need to add mypy for type-check and fix the relevant problems and add relevant unitests. \n",
        "  4. Write scripts for development and deployment enviorment using shell.\n",
        "  5. Configure the relevant EC2 machine (security group, packages installtion).\n",
        "  6. Build the docker images using the relevant docker file.\n",
        "    * Build the container image from 'python:3.6-slim' source image.\n",
        "  7. Deploy the docker container on AWS EC2 (t2.micro).\n",
        "    * Test the application from the local machine as a client.\n",
        "  8. Upload the docker image to dockerhub public repsotory:\n",
        "    * https://hub.docker.com/repository/docker/sagiezri/ecg_pacemaker_classifier\n",
        "\n",
        "\n",
        "* **Prerequisites**:\n",
        "  * Windows:\n",
        "    * Install WSL 2 and Docker Desktop on your local windows machine\n",
        "  * Linux:\n",
        "    * Install docker packages for the relevant OS.\n",
        "  * Download the relevant ecg_predict.txt, .dat, .hea files to the local machine\n",
        "  (from sagi ezri git repostory/assignment email or download and fetch from the dataset).\n",
        "    * Files location for the curl command: \n",
        "      * ecg_pacemaker_mini_project/curl_cmd_files/ \n",
        "    * You need the both .dat amd .hea of a specific sample.\n",
        "    * Create by yourself .txt file, ecg_predict.txt structure (for example):\n",
        "  \n",
        "    {\n",
        "\n",
        "    \"recording_date\":\"01/11/1984  8:27:32\",\n",
        "\n",
        "    \"age\":19,\n",
        "\n",
        "    \"height\":55,\n",
        "\n",
        "    \"weight\": 80,\n",
        "\n",
        "    \"nurse\": 0,\n",
        "\n",
        "    \"site\": 2,\n",
        "\n",
        "    \"device\": \"CS-12   E\",\n",
        "\n",
        "    \"sex\": \"Male\",\n",
        "\n",
        "    \"patient_id\": 1\n",
        "\n",
        "    }\n",
        "\n",
        "\n",
        "* **Executing program**:\n",
        "  * Open CMD\n",
        "  * Run:\n",
        "    * docker pull sagiezri/ecg_pacemaker_classifier:version1.0\n",
        "    * sudo docker run -p 80:3000 sagiezri/ecg_pacemaker_classifier:version1.0\n",
        "  * The docker image should be running automatically and start the service.\n",
        "  * Configure the .txt file with the relevant parameters.\n",
        "  * Update the relevant curl command if needed (using other files).\n",
        "  * Run:\n",
        "    * Test it on your localhost:\n",
        "      * curl -F files=@ecg_predict.txt -F files=@00001_lr.dat -F files=@00001_lr.hea http://localhost/predict\n",
        "    * Test on remote computer:\n",
        "      * curl -F files=@ecg_predict.txt -F files=@00001_lr.dat -F files=@00001_lr.hea http://server-public-dns/predict\n",
        "  * In case you wish to check it on my remote machine - ping me and I'll start the instance."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "klVYkC1suYz5",
        "qybjAtG0-oNo"
      ],
      "machine_shape": "hm",
      "name": "Sagi_ecg_pacemaker_PROJECT.ipynb",
      "provenance": []
    },
    "kernelspec": {
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